Vice President, Data Scientist – Credit Risk, Risk Insights - Chase 360

JPMorgan Chase & Co.•Columbus, OH

14d

About The Position

Join a high-impact team shaping credit strategy across consumer and small business lending at JPMorganChase. Leverage Chase’s cross‑line‑of‑business data and your advanced analytics skills to surface emerging risks, guide portfolio decisions, and protect customers through cycles. You’ll operate at the intersection of data science, macro insights, and credit risk, presenting timely intelligence to senior CCB (Consumer and Community Bank) Risk leaders. If you thrive in dynamic environments and love turning complex data into clear actions, this role is for you. As a Vice President, Data Scientist in the Chase 360 Payment Analytics team within CCB Risk Insights, you will leverage cross‑LOB customer data to generate insights on consumer and small‑business health, inform and influence credit strategy, and build attributes that power credit models and decisions. You will own end‑to‑end analytical work—from data engineering and feature development to modeling, interpretation, and executive storytelling—delivering timely, high‑impact insights and dashboards that influence strategic choices across Card, Auto, Home Lending, and Business Banking. You will independently research emerging risks, synthesize external publications and data releases, and partner across Chase 360 to drive scalable solutions and measurable business outcomes.

Requirements

Advanced degree (MS preferred) in statistics, econometrics, or related quantitative field with minimum 7 years in risk management or quantitative roles; or BS with minimum 8 years relevant experience.
Experience in consumer financial services with a focus on credit risk analytics and portfolio monitoring across the credit lifecycle.
Strong Python proficiency (data wrangling, modeling, visualization, automation) and production‑grade code practices.
Advanced SQL skills with proven ability to query, transform, and QC large, complex datasets from multiple sources.
Demonstrated ability to build, maintain, and validate attributes/features for credit models and strategy execution.
Track record of delivering time‑critical analytical reports/dashboards to senior stakeholders with clear, actionable insights.
Strong quantitative problem‑solving, hypothesis‑driven analysis, and experimental design skills.
Excellent communication skills, translating technical analyses into concise recommendations for leadership.
Self‑starter with ownership mindset; proven ability to drive ambiguous problems to scalable solutions under tight timelines.
Familiarity with integrating external data/publications and macro trends into credit risk assessments.
Project management experience leading cross‑functional initiatives from scoping through delivery and adoption.

Nice To Haves

Experience with payments data, spend behaviors, and early‑warning indicators tied to consumer and small‑business health.
Knowledge of credit risk modeling techniques (e.g., logistic/GBM, survival analysis, scorecarding) and performance monitoring.
Proficiency with data visualization/BI tools (e.g., Tableau, Power BI) and workflow orchestration (e.g., Airflow) for automated reporting.
Familiarity with cloud data platforms and distributed computing (e.g., AWS, Spark) for large‑scale analytics.
Working knowledge of model risk governance and controls for data, features, and performance tracking.
Experience partnering across business lines and risk functions to align strategies and implement analytics at scale.
Strong sense of learning agility—comfort quickly adopting new tools, methods, and business concepts.

Responsibilities

Generate timely insights on consumer and small‑business health using cross‑LOB data to identify, quantify, and monitor emerging credit risks.
Inform and influence credit strategies across Card, Auto, Home Lending, and Business Banking with data‑driven recommendations and scenario analysis.
Engineer and maintain high‑quality attributes/features to support credit models, segmentation, and policy execution.
Design and execute advanced analytics (e.g., risk segmentation, early‑warning signals, stress indicators) to track portfolio trends and headwinds.
Build and automate dashboards and recurring reports that translate complex analytics into clear, actionable leadership narratives.
Conduct independent research on macro, industry, and payment trends; connect external developments to portfolio risks and opportunities.
Analyze peer publications and public data releases to produce differentiated viewpoints for CCB Risk leadership.
Partner with data engineering, model, and product teams to operationalize insights and ensure scalability, resiliency, and governance.
Manage cross‑functional projects end‑to‑end, aligning stakeholders, defining milestones, and delivering on time in a fast‑paced environment.
Communicate findings to technical and non‑technical audiences, using crisp narratives, visuals, and executive‑ready materials.
Champion best practices in code quality, reproducibility, and model/metric documentation to elevate team capabilities.